Insulin sensitivity predictions in individuals with obesity and type II diabetes mellitus using mathematical model of the insulin signal transduction pathway.

Mathematical modeling approaches have been commonly used in complex signaling pathway studies such as the insulin signal transduction pathway. Our expanded mathematical model of the insulin signal transduction pathway was previously shown to effectively predict glucose clearance rates using mRNA levels of key components of the pathway in a mouse model. In this study, we re-optimized and applied our expanded model to study insulin sensitivity in other species and tissues (human skeletal muscle) with altered protein activities of insulin signal transduction pathway components. The model has now been optimized to predict the effect of short term exercise on insulin sensitivity for human test subjects with obesity or type II diabetes mellitus. A comparison between our extended model and the original model showed that our model better simulates the GLUT4 translocation events of the insulin signal transduction pathway and glucose uptake as a clinically relevant model output. Results from our extended model correlate with O'Gorman's published in-vivo results. This study demonstrates the ability to adapt this model to study insulin sensitivity to many biological systems (human skeletal muscle and mouse liver) with minimal changes in the model parameters.

Mathematical modeling of the insulin signal transduction pathway for prediction of insulin sensitivity from expression data.

Mathematical models of biological pathways facilitate a systems biology approach to medicine. However, these models need to be updated to reflect the latest available knowledge of the underlying pathways. We developed a mathematical model of the insulin signal transduction pathway by expanding the last major previously reported model and incorporating pathway components elucidated since the original model was reported. Furthermore, we show that inputting gene expression data of key components of the insulin signal transduction pathway leads to sensible predictions of glucose clearance rates in agreement with reported clinical measurements. In one set of simulations, our model predicted that glycerol kinase knockout mice have reduced GLUT4 translocation, and consequently, reduced glucose uptake. Additionally, a comparison of our extended model with the original model showed that the added pathway components improve simulations of glucose clearance rates. We anticipate this expanded model to be a useful tool for predicting insulin sensitivity in mammalian tissues with altered expression protein phosphorylation or mRNA levels of insulin signal transduction pathway components.

An interactive teaching device simulating intussusception reduction.

Intussusception is relatively uncommon, occurring in 0.5 to 2.3 cases per 1,000 live births in the USA. Radiology residents, therefore, have few opportunities to participate in intussusception reduction during training, and practicing radiologists encounter it infrequently. Training is essential, as successful reduction avoids surgery. The judgment involved in reducing an intussusception is best gained with experience. We developed a training device that simulates fluoroscopic intussusception reduction with air. The device consists of a doll that contains a cylinder with similar stress and strain characteristics to the human colon. The trainee pumps air into the cylinder through a rectal tube using a standard hand-held air reduction pump. A sensor measures the pressure within the chamber and transmits readings to a computer, which displays images from actual intussusception reductions based on the pressure maintained within the device. A random component in the software gives the user a new experience each time and models uncertainties in the actual reduction process, including perforation. This intussusception reduction simulator can enhance resident education, giving residents the opportunity to practice this technique before employing it on a real patient. The simulator can also help practicing radiologists become more comfortable with intussusception air reduction.

Noninvasive multimodal evaluation of bioengineered cartilage constructs combining time-resolved fluorescence and ultrasound imaging.

A multimodal diagnostic system that integrates time-resolved fluorescence spectroscopy, fluorescence lifetime imaging microscopy, and ultrasound backscatter microscopy is evaluated here as a potential tool for assessing changes in engineered tissue composition and microstructure nondestructively and noninvasively. The development of techniques capable of monitoring the quality of engineered tissue, determined by extracellular matrix (ECM) content, before implantation would alleviate the need for destructive assays over multiple time points and advance the widespread development and clinical application of engineered tissues. Using a prototype system combining time-resolved fluorescence spectroscopy, FLIM, and UBM, we measured changes in ECM content occurring during chondrogenic differentiation of equine adipose stem cells on 3D biodegradable matrices. The optical and ultrasound results were validated against those acquired via conventional techniques, including collagen II immunohistochemistry, picrosirius red staining, and measurement of construct stiffness. Current results confirm the ability of this multimodal approach to follow the progression of tissue maturation along the chondrogenic lineage by monitoring ECM production (namely, collagen type II) and by detecting resulting changes in mechanical properties of tissue constructs. Although this study was directed toward monitoring chondrogenic tissue maturation, these data demonstrate the feasibility of this approach for multiple applications toward engineering other tissues, including bone and vascular grafts.